Optimization, i.e., the efficient utilization of assets used in the physical transport of persons and commodities presents an ongoing challenge to organizations involved in transportation services. The effective utilization of deployed assets, e.g., vehicles, is a major objective of any entity engaged in transportation services irrespective of whether the particular transport mode is via an airline, ships, railroad, bus line, trucking service or other form of vehicular transport. Thus, an important consideration in any transportation organization's asset utilization program is the process that assigns a given vehicle type to a particular route within an organization's fleet of vehicles within the routes or service network serviced by the organization. As used herein, the term "network" refers to geographic locations that are served and tied together via a route structure serviced by one or more of the above mentioned transportation means. Consequently, the goal of any vehicle assignment process is to provide the optimal allocation of assets, e.g., vehicle types within an organization's transportation fleet in order to service routes within the network, subject to operational constraints.
The most common form of vehicle or Fleet Assignment Model ("leg based FAM") uses simplifying assumptions relating to passenger/cargo demands, revenues and network flows in order to approximate the expected revenue for each leg of a transportation organization's network. A network leg is best understood as a non-stop service, or route between any two locations within the transportation organization's service network. While these simplifying assumptions provide a point estimate of the expected revenue for each leg in the network given various capacity options, a significant deficiency with this approach is that it does not accurately incorporate Origination and Destination ("O&D") passenger effects. Origination and Destination effects include the impact of transportation over multiple legs of the network. An effective vehicle assignment process should, therefore, account for origination and destination effects by acknowledging and allowing for multiple markets throughout the network, multiple classes of service within market classes and network interactions or impact precipitated by market competition for available space.
Prior attempts to develop an O&D-based FAM formulation combined an O&D passenger flow model and a leg-based FAM model into one mixed integer nonlinear model formulation. The model developed in this early attempt used a brute force computational approach to solve the mixed integer nonlinear model and required hours of mainframe processing time to find a first feasible solution. In order to solve the model, the nonlinear O&D revenue function for each leg in a network was approximated a priori using linear segments. This approach did not account for or anticipate the form of the O&D revenue function prior to solving the model. This drawback was, in turn, was exacerbated by yield management process dependence upon expected demand for each class of service available within the network, and the equipment or vehicle type assigned to each leg of an organization's network. Consequently, this approach proved impracticable for commercial applications.
Thus, there exists a need for a system and method to incorporate origination and destination network effects into a transportation industry vehicle assignment process.